Efficient kernel orthonormalized PLS for remote sensing applications

IEEE Transactions on Geoscience and Remote Sensing

Volumn 46, Issue 10, 2008, Pages 2872-2881

  Arenas García, Jerónimo ^a   Camps Valls, Gustavo ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Feature extraction; Image classification; Kernel methods; Model inversion; Partial least squares (PLS); Support vector machine (SVM)

Indexed keywords

APPLICATIONS; APPROXIMATION ALGORITHMS; BOOLEAN FUNCTIONS; CURVE FITTING; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LEAST SQUARES APPROXIMATIONS; PARAMETER ESTIMATION; REMOTE SENSING; SPACE OPTICS;

KERNEL METHODS; MODEL INVERSION; PARTIAL LEAST SQUARES (PLS); SUPPORT VECTOR MACHINE (SVM);

FEATURE EXTRACTION;

EID: 53949085760     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2008.918765     Document Type: Conference Paper

References (47)

1. I. Hiroaki, N. Toyonori, and T. Eiji, "Measurement of pesticide residues in food based on diffuse reflectance IR spectroscopy," IEEE Trans. Instrum. Meas., vol. 51, no. 5, pp. 886-890, Oct. 2002.
2. Y. Ni, P. Qiu, and S. Kokot, "Simultaneous determination of three organophosphorus pesticides by differential pulse stripping voltammetry and chemometrics," Anal. Chim. Acta, vol. 516, no. 1/2, pp. 7-17, Jul. 2004.
3. P. Geladi, B. Sethson, J. Nyström, T. Lillhonga, T. Lestander, and J. Burger, "Chemometrics in spectroscopy - Part 2: Examples," Spectrochim. Acta B, At. Spectrosc., vol. 59, no. 9, pp. 1347-1357, Sep. 2004.
4. D. Purcell, M. G. O'Shea, and S. Kokot, "Role of chemometrics for at-field application of NIR spectroscopy to predict sugarcane clonal performance," Chemometr. Intell Lab. Syst., vol. 87, no. 1, pp. 113-124, May 2007.
5. N. C. Coops, M.-L. Smith, M. E. Martin, and S. V. Ollinger, "Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1338-1346, Jun. 2003.
6. P. A. Townsend, J. R. Foster, R. A. Chastain, Jr., and W. S. Currie, "Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1347-1354, Jun. 2003.
7. M. D. Wilson, S. L. Ustin, and D. M. Rocke, "Classification of contamination in salt marsh plants using hyperspectral reflectance," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1088-1095, May 2004.
8. E. Naesset, O. Martin Bollandsas, and T. Gobakken, "Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data," Remote Sens. Environ., vol. 94, no. 4, pp. 541-553, Feb. 2005.
9. L. Li, S. L. Ustin, and D. Riaño, "Retrieval of fresh leaf fuel moisture content using genetic algorithm, partial least squares (GA-PLS) modeling," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 216-220, Apr. 2007.
10. S. Wold, C. Albano, W. J. Dunn, U. Edlund, K. Esbensen, P. Geladi, S. Hellberg, E. Johansson, W. Lindberg, and M. Sjostrom, "Multivariate data analysis in Chemistry," in Chemometrics, Mathematics and Statistics in Chemistry. Amsterdam, The Netherlands: Reidel, 1984, p. 17.
11. H. Wold, "Path models with latent variables: The NIPALS approach," in Quantitative Sociology: International Perspectives on Mathematical and Statistical Model Building. New York: Academic, 1975, pp. 307-357.
12. K. Worsley, J. Poline, K. Friston, and A. Evans, "Characterizing the response of PET and fMRI data using multivariate linear models," NeuroImage, vol. 6, no. 4, pp. 305-319, Nov. 1998.
13. P. D. Sampson, A. P. Streissguth, H. M. Barr, F. L. Bookstein, "Neurobehavioral effects of prenatal alcohol - Part II: Partial least squares analysis," Neurotoxicol. Teratol., pp. 477-491, vol. 11, no. 5, Sep./Oct. 1989.
14. P. Geladi, "Notes on the history and nature of partial least squares (PLS) modelling," J. Chemom., vol. 2, no. 4, pp. 231-246, Aug. 1988.
15. A. Wegelin, "A survey of partial least squares (PLS) methods, with emphasis on the two-block case," Univ. Washington, Seattle, WA, Tech. Rep., 2000.
16. R. Rosipal and N. Kramer, "Overview and recent advances in partial least squares," in Proc. Subspace, Latent Structure Feature Selection Techniques, 2006, pp. 34-51.
17. S. Roweis and C. Brody, "Linear heteroencoders," Gatsby Comput. Neurosci. Unit, London, U.K., Tech. Rep. 1999-002, 1999.
18. S. Wold, N. Kettaneh, and B. Skagerberg, "Nonlinear PLS modelling," Chemometr. Intell. Lab. Syst., vol. 7, no. 1/2, pp. 53-65, Dec. 1989.
19. I. E. Frank, "A nonlinear PLS model," Chemolab, vol. 8, pp. 109-119, 1990.
20. G. Baffi, E. B. Martin, and A. J. Morris, "Non-linear projection to latent structures revisited (the neural network PLS algorithm)," Comput. Chem. Eng., vol. 23, no. 9, pp. 1293-1307, Nov. 1999.
21. B. Schölkopf and A. Smola, Learning With Kernels - Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT Press, 2002.
22. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
23. G. Camps-Valls, L. Rojo-Álvarez, and M. Martínez- Ramón, Eds., Kernel Methods in Bioengineering, Signal and Image Processing. Hershey, PA: Idea Group Publishing, Jan. 2007.
24. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
25. G. Camps-Valls, L. Gómez-Chova, J. Muñoz Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
26. J. Muñoz Mari, L. Bruzzone, and G. Camps-Vails, "A support vector domain description approach to supervised classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 8, pp. 2683-2692, Aug. 2007.
27. A. Banerjee, P. Burlina, and C. P. Diehl, "A support vector method for anomaly detection in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 8, pp. 2282-2291, Aug. 2006.
28. G. Camps-Valls, L. Bruzzone, J. L. Rojo-Álvarez, and F. Melgani, "Robust support vector regression for biophysical variable estimation from remotely sensed images," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 3, pp. 339-343, Jul. 2006.
29. F. Yang, M. A. White, A. R. Michaelis, K. Ichii, P. Hashimoto, H. Votava, A.-X. Zhu, and R. R. Nemani, "Prediction of continental-scale evapo-transpiration by combining MODIS and AmeriFlux data through support vector machine," IEEE Trans. Geosci. Remote Sens., vol. 44, pt. 2, no. 11, pp. 3452-3461, Nov. 2006.
30. R. Rosipal and L. J. Trejo, "Kernel partial least squares regression in reproducing kernel Hilbert space," J. Mach. Learn. Res., vol. 2, pp. 97-123, Mar. 2001.
31. W. H. A. M. van den Broek, E. P. P. A. Derks, E. W. van de Ven, D. Wienke, P. Geladi, and L. M. C. Buydens, "Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS)," Chemometr. Intell. Lab. Syst., vol. 35, no. 2, pp. 187-197, Dec. 1996.
32. B. M. Nicolaï, K. I. Theron, and J. Lammertyn, "Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple," Chemometr. Intell. Lab. Syst., vol. 85, no. 2, pp. 243-252, Feb. 2007.
33. J. Arenas-García, K. B. Petersen, and L. K. Hansen, "Sparse kernel ortho-normalized PLS for feature extraction in large data sets," in Advances in Neural Information Processing Systems, vol. 19, B. Schölkopf, J. C. Platt, and T. Hofmann, Eds. Cambridge, MA: MIT Press, 2007.
34. J. Arenas-García and G. Camps-Valls, "Feature extraction from remote sensing data using kernel orthonormalized PLS," in Proc. IEEE IGARSS, Jul. 2007, pp. 258-261.
35. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
36. J. Mercer, "Functions of positive and negative type and their connection with, the theory of integral equations," Philos. Trans. R. Soc. London A, Math. Phys. Sci., vol. CCIX, no. A456, pp. 215-228, May 1905.
37. M. A. Aizerman, E. M. Braverman, and L. I. Rozoner, "Theoretical foundations of the potential function method in pattern recognition learning," Autom. Remote Control, vol. 25, pp. 821-837, 1964.
38. L. Hoegaerts, J. A. K. Suykens, J. Vanderwalle, and B. De Moor, "Primal space sparse kernel partial least squares regression for large scale problems," in Proc. Int. Joint Conf. Neural Netw., 2004, p. 563.
39. M. Momma and K. Bennet, "Sparse kernel partial least squares regression," in Proc. COLT, 2003, pp. 216-230.
40. Y.-J. Lee and O. L. Mangasarian, "RSVM: Reduced support vector machines," Data Mining Inst., Comp. Sci. Dept., Univ. Wisconsin, Madison, WI, Tech. Rep. 00-07, 2000.
41. Y.-J. Lee and S.-Y. Huang, "Reduced support vector machines: A statistical theory," IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 1-13, Jan. 2007.
42. R. Rifkin and A. Klautau, "In defense of one-vs-all classification," J. Mach. Learn. Res., vol. 5, no. 1, pp. 101-141, Jan. 2004.
43. J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. Carder, S. A. Garver, M. Kahru, and C. McClain, "Ocean color chlorophyll algorithms for SeaWiFS," J. Geophys. Res., vol. 103, no. C11, pp. 24 937-24 953, Oct. 1998.
44. P. Cipollini, G. Corsini, M. Diani, and R. Grass, "Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1508-1524, Jul. 2001.
45. LAI-2000 Plant Canopy Analyzer Instruction Manual, LI-COR, Lincoln, NE, 1992. Tech. Rep.
46. J. P. G. W. Clevers, "The derivation of a simplified reflectance model for the estimation of leaf area index," Remote Sens. Envimn., vol. 25, no. 1, pp. 53-69, Jun. 1988.
47. F. Vuolo, G. D'Urso, and L. Dini, "Cost-effectiveness of vegetation bio-physical parameters retrieval from remote sensing data," in Proc. IEEE IGARSS, Jul. 2006, pp. 1949-1952.